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ISCAS
2005
IEEE
214views Hardware» more  ISCAS 2005»
13 years 11 months ago
Blind separation of statistically independent signals with mixed sub-Gaussian and super-Gaussian probability distributions
— In the context of Independent Component Analysis (ICA), we propose a simple method for online estimation of activation functions in order to blindly separate instantaneous mixt...
Muhammad Tufail, Masahide Abe, Masayuki Kawamata
CIARP
2006
Springer
13 years 8 months ago
Robustness Analysis of the Neural Gas Learning Algorithm
The Neural Gas (NG) is a Vector Quantization technique where a set of prototypes self organize to represent the topology structure of the data. The learning algorithm of the Neural...
Carolina Saavedra, Sebastián Moreno, Rodrig...
NIPS
2008
13 years 7 months ago
Bayesian Kernel Shaping for Learning Control
In kernel-based regression learning, optimizing each kernel individually is useful when the data density, curvature of regression surfaces (or decision boundaries) or magnitude of...
Jo-Anne Ting, Mrinal Kalakrishnan, Sethu Vijayakum...
SIAMSC
2008
237views more  SIAMSC 2008»
13 years 6 months ago
A Variational Shape Optimization Approach for Image Segmentation with a Mumford--Shah Functional
We introduce a novel computational method for a Mumford-Shah functional, which decomposes a given image into smooth regions separated by closed curves. Casting this as a shape opti...
Günay Dogan, Pedro Morin, Ricardo H. Nochetto
TNN
2010
234views Management» more  TNN 2010»
13 years 27 days ago
Novel maximum-margin training algorithms for supervised neural networks
This paper proposes three novel training methods, two of them based on the back-propagation approach and a third one based on information theory for Multilayer Perceptron (MLP) bin...
Oswaldo Ludwig, Urbano Nunes